Gambling problems predict suicidality in recently transitioned military veterans
Abstract: This study investigated associations between gambling problems and suicidality in Australian veterans. Data drawn from n = 3,511 Australian Defence Force veterans who had recently transitioned to civilian life. Gambling problems were assessed using the Problem Gambling Severity Index (PGSI) and suicidal ideation and behaviour were assessed using items adapted from the National Survey of Mental Health and Wellbeing. At-risk gambling and problem gambling were associated with increased odds of suicidal ideation [at-risk gambling: odds ratio (OR), 1.93; 95% confidence interval (CI), 1.47‒2.53; problem gambling: OR, 2.75; 95% CI 1.86‒4.06] and suicide planning or attempts (at-risk gambling: OR, 2.07; 95% CI, 1.39‒3.06; problem gambling: OR 4.22, 95% CI, 2.61‒6.81). The association with total scores on the PGSI and any suicidality was substantially reduced and became non-significant when controlling for the effects of depressive symptoms, but not financial hardship or social support. Gambling problems and harms are important risk factors for suicide in veterans, and should be recognised in veteran-specific suicide prevention policies and programs, along with co-occurring mental health problems. A comprehensive public health approach to reducing gambling harm should feature in suicide prevention efforts in veteran and military populations.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.